92 citations · 161 across the 8 of their papers we have counts for
8 papers
A Multi-Dataset Benchmark for Evaluating LLM Agents in Microservice Failure Diagnosis
Yuanhong Cai, Xiaohui Nie, Kanglin Yin +8
LLM-based agents are reshaping microservice operations into AgentOps, where benchmarks are key to evaluating failure diagnosis over multimodal observability data. However, existing…
UModel: An Agent-Ready Observability Data Modeling Method at Scale
Changhua Pei, Zheyuan Li, Zexin Wang +10
When networked system failures occur, automatically performing Root Cause Analysis (RCA) using observability data is critical for ensuring networked system reliability. Recently, L…
TrioXpert: An Automated Incident Management Framework for Microservice System
Yongqian Sun, Yu Luo, Xidao Wen +5
Automated incident management plays a pivotal role in large-scale microservice systems. However, many existing methods rely solely on single-modal data (e.g., metrics, logs, and tr…
A Scenario-Oriented Benchmark for Assessing AIOps Algorithms in Microservice Management
Yongqian Sun, Jiaju Wang, Zhengdan Li +9
AIOps algorithms play a crucial role in the maintenance of microservice systems. Many previous benchmarks' performance leaderboard provides valuable guidance for selecting appropri…
OpsEval: A Comprehensive IT Operations Benchmark Suite for Large Language Models
Yuhe Liu, Changhua Pei, Longlong Xu +13
Information Technology (IT) Operations (Ops), particularly Artificial Intelligence for IT Operations (AIOps), is the guarantee for maintaining the orderly and stable operation of e…
Actionable and Interpretable Fault Localization for Recurring Failures in Online Service Systems
Zeyan Li, Nengwen Zhao, Mingjie Li +11
Fault localization is challenging in an online service system due to its monitoring data's large volume and variety and complex dependencies across or within its components (e.g.,…
Causal Inference-Based Root Cause Analysis for Online Service Systems with Intervention Recognition
Mingjie Li, Zeyan Li, Kanglin Yin +4
Fault diagnosis is critical in many domains, as faults may lead to safety threats or economic losses. In the field of online service systems, operators rely on enormous monitoring…
DOI: Divergence-based Out-of-Distribution Indicators via Deep Generative Models
Wenxiao Chen, Xiaohui Nie, Mingliang Li +1
To ensure robust and reliable classification results, OoD (out-of-distribution) indicators based on deep generative models are proposed recently and are shown to work well on small…